Cloud based schema mapping feature store

    公开(公告)号:US12111798B2

    公开(公告)日:2024-10-08

    申请号:US17587346

    申请日:2022-01-28

    IPC分类号: G06F16/21 G06F40/295

    CPC分类号: G06F16/212 G06F40/295

    摘要: Embodiments map a source schema to a target schema using a feature store. Embodiments receive a file including a plurality of source schema elements and a plurality of target schema elements, the file including a plurality of unmapped elements. Embodiments retrieve rule based mappings for the unmapped elements between the source schema elements and the target schema elements. Based on semantic matching of the source schema elements, embodiments retrieve feature store based mappings from the feature store for the unmapped elements between the source schema elements and the target schema elements. Embodiments then generate one or more similarity scores for mappings of the source schema elements to the target schema elements.

    Machine Learning Based Genomics Test Predictor

    公开(公告)号:US20240331806A1

    公开(公告)日:2024-10-03

    申请号:US18192896

    申请日:2023-03-30

    IPC分类号: G16B40/20

    CPC分类号: G16B40/20

    摘要: Embodiments predict genomic testing using machine learning. Embodiments receive one or more training datasets of a genomic pipeline comprising a plurality of training variables for each of a plurality of genomic tests and corresponding results of each of the genomic tests. Embodiments train a machine learning model using the training datasets and receive a new genomic workflow pipeline comprising new genomic testing variables. Embodiments then predict, using the trained machine learning model and new genomic testing variables, whether the new genomic workflow pipeline will be successfully completed within a first compute environment.

    SYSTEM AND TECHNIQUES FOR TRAVERSING A DEPENDENCY GRAPH OR TREE STRUCTURE IN ONE STEP

    公开(公告)号:US20240330266A1

    公开(公告)日:2024-10-03

    申请号:US18130419

    申请日:2023-04-03

    IPC分类号: G06F16/23 G06F16/2457

    CPC分类号: G06F16/2358 G06F16/24573

    摘要: In some aspects, techniques can be performed by a processor of a computing device, the method can include receiving an input selecting a first node instance of a first node. The method can include accessing a node dependency table listing one or more parent nodes and child nodes for the selected first node instance. The method can include determining if dependencies for the selected first node instance are met by accessing a node process log. The technique can include running the first node when the dependencies for the selected first node are met. The technique can include updating the node process log.

    BUILD SYSTEM FOR SECURELY BUILDING AND DEPLOYING A TARGET BUILD ARTIFACT

    公开(公告)号:US20240329953A1

    公开(公告)日:2024-10-03

    申请号:US18190331

    申请日:2023-03-27

    IPC分类号: G06F8/41

    CPC分类号: G06F8/433

    摘要: A build system is disclosed that identifies the inputs used by a build process for securely building and deploying a piece of software to production. The build system comprises a build container and a build proxy server. The build container receives a set of initial inputs for performing a build and generates a build output (e.g., a target artifact) as a consequence of performing the build. The build proxy server monitors both internal interactions as well as external interactions (e.g., input dependency fetches from external artifact repositories) of the build container within and outside a network boundary defined around the build container. Based on the monitored interactions, the build proxy server identifies all the additional input components and/or input component dependencies used by the build container for successfully performing the build. The build container uses the identified components to perform the build and generate a target artifact.

    Pseudo labelling for key-value extraction from documents

    公开(公告)号:US12106595B2

    公开(公告)日:2024-10-01

    申请号:US18379091

    申请日:2023-10-11

    IPC分类号: G06V30/414 G06V30/19

    摘要: A computing device may access visually rich documents comprising an image and metadata. A graph, based on the image or metadata, can be generated for a visually rich document. The graph's nodes can correspond to words from the visually rich document. Features for nodes can be determined by the device. The device may generate model labeled graphs by assigning a pseudo-label to nodes using a pretrained model. The device may generate a plurality of graph labeled graphs by assigning a pseudo-label to nodes by matching a first node from a first graph to at least a second node from a second graph. The device may generate a plurality of updated graphs by cross referencing labels from the model labeled graphs and the graph labeled graphs. Until a change in labels is below a threshold, a model can be trained to perform key-value extraction using the updated graphs.